Kashf Journal of Multidisciplinary Research
The Kashf Journal of Multidisciplinary Research, published by the Kashf Institute of Development & Studies, is an HEC-recognized multidisciplinary journal. It serves as a platform for scholarly exploration across various disciplines, accepting research papers that undergo a rigorous peer-review process. KJMR is a platform for scholarly exploration across various disciplines. It accepts research papers that undergo a rigorous peer-review process and publishes findings relevant to academics, policymakers, and practitioners. This journal encourages cross-pollination of ideas between different fields, potentially impacting areas like social sciences, humanities, natural sciences, and engineering. Overall, it contributes to advancing knowledge, informing practice, and stimulating innovation across a broad spectrum of disciplines. e-ISSN: 3007-200X p-ISSN: 3007-1992
KASHF INSTITUTE OF DEVELOPMENT & STUDIES
*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id="request-WEB:c3ec9473-a735-4ccd-952d-fe9dc80c0402-2" data-testid="conversation-turn-6" data-scroll-anchor="true" data-turn="assistant"> KJMR publishes all articles as open access under CC BY 4.0, allowing anyone to share and adapt the work, even commercially, with proper credit, a license link, and clear notice of changes, without implying endorsement. Authors retain copyright while granting the journal non-exclusive publishing and archiving rights and may self-archive without embargo. Third-party material requires proper permission, and the journal ensures long-term free access through its website and archiving partners.
CYBERSECURITY CHALLENGES AND DEFENSE MECHANISMS IN AUTONOMOUS VEHICLES: PROTECTING CONNECTED AND INTELLIGENT TRANSPORTATION SYSTEMS FROM EMERGING CYBER THREATS
The concept of autonomous vehicles (AVs) has introduced a new and advanced technology in the field of intelligent transportation systems. It has the potential to integrate artificial intelligence, sensor technologies, and communication systems. However, the increased level of connectivity and dependency on digital technologies have led to the emergence of various cybersecurity threats. Malicious actors can take advantage of the vulnerabilities in the communication network of the vehicles and the architecture of the autonomous vehicles. This paper identifies the major cybersecurity threats in the context of autonomous vehicles. They include Controller Area Network (CAN) attacks, denial-of-service (DoS) attacks, GPS spoofing, and the vulnerability of the vehicle-to-everything (V2X) communication. A multilayered cybersecurity architecture based on encryption, authentication, and intrusion detection using machine learning has been proposed. The effectiveness of the proposed architecture has been validated through experimentation using the simulated network traffic of the autonomous vehicles. It has demonstrated the potential of the Random Forest algorithm in detecting malicious activities in the network.
Copyright (c) 2026 Muhammad Ahsan Hayat, Mehak Riaz Khan, Nazia Alfred Fernandes, Maryam Shaikh, Imad Ali, Engr. Sidra Rehman (Author) https://creativecommons.org/licenses/by/4.0
EXPLOITING QUALITY OF PRODUCTS TO IDENTIFY CUSTOMER’S ATTITUDE USING MACHINE LEARNING METHODS
With the rise of e-commerce and online shopping, consumers are increasingly relying on the reviews and feedback of other customers to make informed decisions about the products they are considering purchasing. While product descriptions, pictures, and videos can be helpful, they may not provide enough detail or nuance for consumers to fully evaluate a product's quality and complexity. As a result, online shops and independent review websites are collecting and aggregating customer feedback to provide a more comprehensive picture of a product's strengths and weaknesses, which can be especially helpful for consumers who are unable to see or handle a product in person before making a purchase. The proposed work is focused on using natural language processing and machine learning methods to automatically identify the quality and complexity of products using consumers' reviews. The proposed study involves a semantic and contextual analysis of consumers' reviews using an appraisal framework. This approach aims to identify and evaluate the opinions and attitudes expressed by consumers in their reviews of products. The appraisal framework is a linguistic approach that considers how language is used to convey judgments, feelings, and evaluations, and can be used to analyze the opinions expressed in written or spoken text. Using machine learning to analyze customer feedback can be an effective way to extract valuable insights and identify patterns that might be difficult to discern using manual methods. The main contribution of this work is preparation of manually annotated datasets for machine learning methods to automatically identify product quality and complexity. The datasets were tested using machine learning methods: Logistic Regression (LR), Support Vector Machine (SVM), Multinomial Naïve Bayes (NB) and Bernoulli NB. In experiments Multinomial NB with over sampling technique ADASYN is compared with other models and achieving average macro F1 score of 0.77 in respect of quality of product.
Copyright (c) 2026 Dr. Amanullah, Dr. Muhammad Javed, Dr. Muhammad Nawaz Khan, Shoukat Ullah, Raja Iqbal (Author) https://creativecommons.org/licenses/by/4.0
NON-CARCINOGENIC HEALTH RISK ASSESSMENT OF HEAVY METALS IN VEGETABLES CULTIVATED IN DISTRICT GHOTKI, SINDH, PAKISTAN
The cultivation of vegetables in District Ghotki, Sindh, Pakistan, is a cornerstone of the local economy but faces significant food safety challenges due to heavy metal contamination. This study conducted a non-carcinogenic health risk assessment of seven metals—Iron (Fe), Zinc (Zn), Manganese (Mn), Copper (Cu), Tin (Sn), Cobalt (Co), and Arsenic (As)—across 15 vegetable varieties. Utilizing Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES), the research quantified metal concentrations and evaluated human exposure through the Estimated Daily Intake (EDI), Target Hazard Quotient (THQ), and Hazard Index (HI). While essential micronutrients (Fe, Zn, Mn, Cu) and trace elements (Sn, Co) remained within safe WHO/FAO limits, Arsenic concentrations in several vegetables, including Lotus, Cauliflower, and Mustard, exceeded permissible thresholds. The THQ for Arsenic surpassed 1.0 in five varieties, identifying it as the primary toxic contributor. Furthermore, the cumulative Hazard Index exceeded 1.0 for 10 out of 15 vegetables, indicating potential long-term health risks for the local population. Principal Component Analysis (PCA) and Pearson correlation confirmed that while essential minerals often co-occur, Arsenic contamination likely originates from independent external sources.
Arsenic (As) Principal Component Analysis (PCA)
Copyright (c) 2026 Tahmina Fakhur-Un-Nisa Abbasi, Ghulam Qadir Shar, Shamroz Bano Sahito, Dr. Abdul Raheem Shar, Mubeen Ahmed Solangi, Iqra Kareem Sahito, Mohbat Ali Mahar, Faiqa Mukhtiar, Zahid Hussain Palh, Ghulam Hassan Wassan (Author) https://creativecommons.org/licenses/by/4.0
PREVALENCE & ASSOCIATION OF COGNITIVE DYSFUNCTION WITHCERVICAL DYSTONIA: A CROSS-SECTIONAL STUDY
Background: Cervical dystonia (CD) is a chronic neurological movement disorder characterized by involuntary contractions of the neck muscles, resulting in abnormal head postures and movements. While motor manifestations are well recognized, non-motor symptoms (particularly cognitive dysfunction) remain underexplored. Cognitive impairment may substantially affect daily functioning, social interaction, and overall quality of life in individuals with CD, highlighting the need for comprehensive evaluation. Objective: The objective of this study is to determine the prevalence and association of cognitive dysfunction in people with cervical dystonia. Methodology: A cross-sectional study was conducted among 73 patients diagnosed with cervical dystonia in Karachi, Pakistan. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and motor symptom severity was evaluated using the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS). Descriptive statistics were calculated, and inferential analysis using the Pearson chi-square test was performed to assess associations between cognitive impairment and disease severity, age, and gender. A p-value < 0.05 was considered statistically significant. Result: A total of 73 participants (36 males and 37 females) were included, with the majority aged 18–29 years (42.5%). Based on TWSTRS, moderate cervical dystonia was most prevalent (63.0%), followed by severe (23.3%) and mild (13.7%). MMSE results showed mild cognitive impairment in 38.4% of participants, severe impairment in 31.5%, and no impairment in 30.1%. Statistical analysis demonstrated a significant association between cognitive function and dystonia severity (χ² = 63.216, p < 0.001) as well as age (p = 0.023), whereas no significant associations were observed with gender (p = 0.704) Conclusion: Cognitive impairment is highly prevalent among patients with cervical dystonia and is significantly associated with both age and disease severity. These findings underscore the importance of incorporating cognitive assessment into routine clinical evaluation and adopting a multidisciplinary approach that addresses both motor and non-motor manifestations of cervical dystonia.
Copyright (c) 2026 Aliza Qureshi , Dr Khadija Usman, Dr Okasha Anjum (Author) https://creativecommons.org/licenses/by/4.0/
SECURING ARTIFICIAL INTELLIGENCE AGAINST INTELLIGENT ADVERSARIES: ROBUST LEARNING FRAMEWORKS FOR ADVERSARIAL, POISONING, AND MODEL EXTRACTION ATTACKS
The rapid proliferation of artificial intelligence (AI) systems across critical domains has heightened concerns regarding their vulnerability to intelligent adversaries. This study evaluated the robustness of machine learning models against adversarial (evasion), poisoning, and model extraction attacks and proposed a multi-layered robust learning framework to mitigate these threats. Experimental results demonstrated that baseline models experienced accuracy degradation of up to 37.6% under PGD adversarial attacks, while poisoning contamination reduced performance by more than 23% and produced backdoor trigger success rates exceeding 92%. Model extraction fidelity reached 89.3%, indicating substantial intellectual property risks. The proposed framework integrated adversarial training, anomaly-based data sanitization, and privacy-preserving output perturbation mechanisms. Following implementation, adversarial robustness improved by up to 26.3%, poisoning attack success rates declined below 13%, and extraction fidelity decreased by 24.2%. Importantly, these improvements were achieved with less than 3% reduction in clean-data accuracy, confirming that enhanced security did not significantly compromise predictive utility. Statistical analysis indicated that robustness improvements were significant at p < 0.05 across all attack categories. The findings emphasized that defense-in-depth architectures provided superior resilience compared to isolated mitigation techniques. The study contributed to secure and trustworthy AI development by presenting a scalable framework capable of addressing multi-stage intelligent adversarial threats while maintaining operational performance stability.
Copyright (c) 2026 Asif Ahmad, Khair Muhammad Saraz, Imran Khan, Shadia Saad Baloch, Amber Baig, Ghulam Nabi (Author) https://creativecommons.org/licenses/by/4.0
FINITE ELEMENT ANALYSIS OF INCOMPRESSIBLE FLUID FLOWS IN A BACKWARD STEP DUCT USING COMSOL MULTIPHYSICS
This study presents a numerical investigation of Newtonian fluid flow through a backward step duct using the Galerkin Least Square Finite Element Method (GLSFEM) within the COMSOL MultiPhysics environment. The research explores the impact of fluid inertia and geometric variations on flow characteristics, specifically focusing on aspect ratios of 1:6 and 1:12. By solving the incompressible stationary and non-stationary Navier-Stokes equations, the simulation characterizes vortex formation, recirculation flow rates, and reattachment lengths across a range of Reynolds numbers. Results indicate that increasing the Reynolds number and expansion ratio significantly enhances the intensity and length of primary and secondary eddies at the duct corners. Quantitative analysis is supported by newly developed empirical equations for recirculation flow rates, which demonstrate high correlation with existing literature. The findings provide critical insights for industrial applications involving flow separation and mixing, such as automotive silencers, chemical transport tanks, and turbine blade design.
Galer kin Least Square Finite Element Method
Copyright (c) 2026 Murtaza Hussain Shar, Akhalque Ahmed Abbasi, Mazhar Ali Sahito, Dr. Abdul Raheem Shar, Muzamil Hussain Shar, Rabia Parveen Memon (Author) https://creativecommons.org/licenses/by/4.0
SCALABLE MACHINE LEARNING FOR TEMPERATURE-DEPENDENT CONVERSIONS IN COBALT-CATALYZED METHANE DRY REFORMING
Predictions of methane dry reforming (DRM) over cobalt catalysts through machine learning have been limited by data sparsity, and the original 57-point benchmark has limited test accuracy (R2=0.42-0.67) because predictions and experimental measurements are overfitting on limited experimental measurements. The research generates a 402-point temperature-dependent dataset (550-750°C) that eliminates this fundamental tradeoff, providing ensemble forecasts with unprecedented R2=0.971 (CH4 conversion) and R2=0.958 (CO2 conversion), which are 70 and 80% better, respectively. Polynomial baseline gradient boosting (XGBoost/LightGBM) models outperform neural architectures by direct control over overfitting ( -2<0.055 vs 0.33-0.48), and SHAP analysis proves kinetic dominance of temperature (98.7% kinetic) allows single-sensor control of processes, compared to the complexity of multi-variable neural networks previously, while acceptable consistency of 10-fold cross-validation ( -2≈0.008) and production scale stability. The temperature-only model yields Arrhenius activation, kinetic acceleration, and equilibrium plateau with less than 2% accuracy, measuring the systematic 5-10% lag of CO2 in greenhouse gas fuels to achieve the desired yield plateau. This gracefully adequate design methodology makes frontier performances with 1/7th of the current literature 300–1000-point data requirements and multidimensional instrumentation requirements to deploy a digital twin of DRM, where research percent conversion accuracy translates to 0.15/kg of syngas cost reduction under real-world thermal gradient conditions of ±50oC. The open-source pipelines democratize entry, where the catalyst screening is guided, whilst the scale of datasets is defined to be the final choice in reproducible catalytic forecasting.
Copyright (c) 2026 Subhan Azeem, Nadeem Hassan, Muhammad Ashraf (Author) https://creativecommons.org/licenses/by/4.0
STUDY ON PREVALENCE OF GASTRO INTESTINAL HELMINTHS OF LARGE AND SMALL RUMINANTS IN DISTRICT TANDO ALLAHYAR, SINDH
The present study was conducted to determine the prevalence of gastrointestinal helminths in large and small ruminants reared in District Tando Allahyar, Sindh, Pakistan. A total of 640 fecal samples were collected from cattle (n=365), buffaloes (n=95), goats (n=135), and sheep (n=45) and examined using standard parasitological techniques. Overall, 472 animals were found positive, showing a prevalence of 73.75%. Species-wise analysis revealed the highest prevalence in goats (81.48%), followed by buffaloes (74.73%), cattle (71.78%), and sheep (64.44%). Statistical analysis indicated a significant association between host species and infection rate (χ² = 8.64, p = 0.034). Age-wise prevalence showed significantly higher infection rates in adult animals compared to younger groups in both large (χ² = 12.18, p = 0.006) and small ruminants (χ² = 9.27, p = 0.026). The predominant parasites identified were Eimeria spp., Ascaris spp., Strongyle spp., Fasciola spp., Trichuris spp., Moniezia spp., and Schistosoma bovis. The high prevalence indicates poor management practices and favorable climatic conditions for parasite survival. Strategic deworming, improved farm hygiene, and regular monitoring are recommended to reduce economic losses associated with gastrointestinal helminthiasis.
Copyright (c) 2026 Rameez Raja Kaleri, Sajjad Ahmed, Zahid Ali Mangrio, Dua Qureshi, Fahad Nawaz, Muhammad Jamal, Mairaj Hussain, Ghulam Abbas, Hassam Ud Din (Author) https://creativecommons.org/licenses/by/4.0
DIGITAL TRANSATION IN HIGHER EDUCATION: A QUALITATIVE INQUIRY INTO TEACHERS’ PERSPECTIVES ON ONLINE TEACHING COMPATABILITY IN ISLAMABAD, PAKISTAN
The aim of this research was to explore teachers’ perspectives on compatibility with online teaching at the higher education level and to identify the challenges they encountered while adapting to online instruction. To achieve this objective, a qualitative research design was employed for data collection. The sample comprised of six participants were selected thorough convenience sampling technique. Six semi-structured face-to-face interviews were conducted to examine teachers’ perspectives on their compatibility with online teaching in higher education. The data underwent inductive analysis; responses were coded and categorized based on emerging patterns, and the findings were presented thematically. To ensure data credibility, member checking was conducted, whereby key informants reviewed and confirmed the findings. Additionally, an audit trail was maintained, consisting of a detailed and chronological record of all research procedures and data analysis decisions, which was presented to two independent reviewer researchers. The findings revealed that traditional, in-person teaching methods emphasizing immediate feedback, real-time interaction, and active student engagement were deeply ingrained in higher education educators as they transitioned to online instruction. Although conventional approaches such as lectures, whiteboard explanations, guided questioning, and practical exercises formed the foundation of their teaching identities, the shift to online environments required substantial adaptation rather than direct transfer. The teachers also highlighted significant challenges, particularly technical difficulties in distance education and complications related to online evaluation and assessment. The study recommends the organization of regular workshops to enhance teachers’ proficiency in utilizing online platforms, managing virtual classrooms, and designing engaging digital lessons. Furthermore, universities should adopt a blended approach that integrates face-to-face instruction with online resources, using each format where it is most effective to improve teaching compatibility and student engagement. Participants emphasized that for online education to function as a reliable and effective complement to conventional teaching methods, it is essential to strengthen technical infrastructure, enhance teachers’ digital competencies, and develop secure and practical assessment tools.
Copyright (c) 2026 Sana Aftab, Mujtaba Javed Iqbal (Author) https://creativecommons.org/licenses/by/4.0
DISCRETIZATION OF LAPLACIAN OPERATOR IN POLAR COORDINATE SYSTEM, USING CRANK-NICOLSON’S (CN) SCHEME AND STABILITY ANALYSIS
Laplacian operator plays a vital role for describing and solving many mathematical models. Finite difference Scheme of Laplacian operator has been carried out by various researchers using Euler’s scheme but unfortunately the scheme is stable for very small ratio of k/h2. However, Crank-Nicolson’s scheme has been proved stable for all values of k/h2. In this contribution discretization of Laplacian operator in polar coordinate system will be carried by using Crank-Nicolson’s Scheme. Stability analysis has been carried out for the scheme and results are compared with previous research analysis. The obtained isotropic discretized laplacian operator on the 5-points stencil on the polar grid system shows stable and accuracy as compared to previously obtained discretization using explicit finite difference scheme. The result obtained on various sizes of polar mesh system shows that the error of the isotropic discrete laplacian decreases rapidly with each step time step of the computation scheme. The derived isotropic discretized laplacian scheme on the polar net system is highly useful for employing and studying various computational models. The acquired discretization of the polar laplacian scheme on the circular annular plane is potential candidate for significant results for the CDS (Cell Dynamic Simulations) model used for advanced materials and image processing used for computational studies. Centre of the circle and its neighborhood have been avoided in this study due to the limitations of laplacian operator in the polar coordinate system.
Copyright (c) 2026 Raunaque Ali Rid, Mazhar Ali Sahito, Akhlaque Ahmed Abbasi, Afshan Parveen Dehraj, Majida Mallah (Author) https://creativecommons.org/licenses/by/4.0
Stacked ANN Modeling for Catalyst Optimization and Methane Conversion Prediction in TRM
Tri-reforming of methane (TRM) is an attractive method to achieve syngas production out of biogas and flue gases, which combines dry reforming, steam reforming, and partial oxidation to produce tunable H2/CO ratios used in Fischer-Tropsch synthesis. Nevertheless, the catalyst deactivation through coking and sintering under mixed feeds requires commercialization to be hindered, and data-driven surrogates are required to elucidate interactions between multiple variables among heterogeneous datasets. This study presents a stacked artificial neural network (ANN) framework to predict CH4 conversion, CO2 conversion and H2/CO ratios based on the base learners trained by the data of component reaction (DRM, SRM, POX) to overcome the problem of data sparseness of TRM series, after preprocessing (wide to long reshaping, removing outliers, and standardization) to obtain unified input consisting of temperature (500-900oC) and the one-hot encoded series identifiers. Base ANN (ReLU hidden layers, Adam optimization) was used to generate the predictions, which were fed to a meta-learner (Ridge regression or shallow ANN) for ensemble blending. At under 4-fold cross-validation, the model converted CH4, CO2, and H2 RMSE of 2.5 +0.3, R2 0.985; CO2, H2 RMSE 3.1 +0.4, R2 0.972; and H2/CO 0.12 +0.02, R2 0.992, parity plots with >95% prediction in the error range of +5%, and unbiased residuals This interpretable, scalable surrogate is better than single-model baselines and fills literature gaps and provides a roadmap to hybrid ML-kinetics in the design of Ni-based TRM catalysts and process integration.
Copyright (c) 2026 Subhan Azeem, Nadeem Hassan, 3Muhammad Ashraf (Author) https://creativecommons.org/licenses/by/4.0
GREEN SYNTHESIS OF SILVER NANOPARTICLES USING PROPOLIS EXTRACT: EVALUATING ANTIMICROBIAL AND ANTIOXIDANT POTENTIAL
The development of eco‑friendly and sustainable nanomaterials has gained significant attention due to increasing concerns regarding the environmental and biological toxicity of chemically synthesized nanoparticles. In the present study, silver nanoparticles (AgNPs) were synthesized via a green synthesis approach using propolis extract as a natural reducing and stabilizing agent. Propolis, a resinous product collected by honeybees, is rich in polyphenols, flavonoids, and bioactive compounds that facilitate nanoparticle formation while imparting biological functionality. The synthesized AgNPs were characterized using UV–Visible spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), X‑ray Diffraction (XRD), and Scanning Electron Microscopy (SEM). UV–Vis analysis confirmed AgNP formation through the appearance of a characteristic surface plasmon resonance band. Structural and morphological analyses revealed crystalline, predominantly spherical nanoparticles with nanoscale dimensions. The antimicrobial activity of the propolis‑mediated AgNPs was evaluated against selected Gram‑positive and Gram‑negative bacterial strains using the agar well diffusion method, demonstrating enhanced inhibitory effects compared to crude propolis extract. Antioxidant potential was assessed using DPPH and ABTS radical scavenging assays, showing concentration‑dependent antioxidant activity. The results suggest that propolis‑derived AgNPs possess significant antimicrobial and antioxidant properties, highlighting their potential application in biomedical, pharmaceutical, and environmental fields.
Copyright (c) 2026 Ali Asgher Shuja, Aqsa Ashfaq, Engr Dr Sana Saeed (Author) https://creativecommons.org/licenses/by/4.0
WHEN SILENCE MEETS PRESENCE: CASE STUDY OF DIGITAL-ERA DEVELOPMENTAL DISRUPTION IN EARLY CHILDHOOD
This is the Era of digital technology, in which adults as well as children are more exposed to digital gadgets than to human presence. Digital gadgets like smart televisions, mobiles, and tablets. This exposure, when given at an early age, can cause developmental disruptions in children. It includes delayed language, inappropriate speech, decreased reciprocity, and behaviors like those seen in autism. This is a case study of a 4-year-old boy who was exposed to the screen at a very early age, , resulting in profound silence, social withdrawal, and behaviors portraying virtual autism. Guided by Parse's human becoming theory, the nurse ensured her presence to Ali and his family and helped them recognize how convenience, quietness, and digital toxicity have restored their views and expectations of childhood development. Applying Parse's processes, bridging understanding, coordinating flows, and inspiring transcendence, the family noticed their habitual ways of living. Together, they created innovative ways to interact, play, and connect while reducing unsupervised screen time. Over the course of six months, Ali has changed dramatically, from being quiet to being vocal, from being solitary to being participative, and from being inexpressive to being expressive. In the light of Parse's theory, this case sets an example of how authentic presence can help families to overcome screen-related developmental changes, assist in environmentally driven communication delays and transform everyday routines in the digital Era.
Copyright (c) 2026 Fatima Soomro, Rafat Jan, Salma Rattani, Amir Rahman, Jalal Khan (Author) https://creativecommons.org/licenses/by/4.0
CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES FOR ROBUST IMAGE FEATURE REPRESENTATION IN RETRIEVAL SYSTEMS
Machine learning techniques have become fundamental to modern image classification and retrieval systems, enabling the extraction of meaningful and discriminative visual features from large image collections. In this research, we propose an efficient framework for generating image descriptors using deep Convolutional Neural Network (CNN) architectures, specifically GoogleNet , Inception V3 , and DenseNet-201 . These pre-trained deep models are utilized to capture multi-level visual characteristics, including fine texture patterns, shape cues, and high-level object semantics. To further strengthen the descriptive power of the extracted features, information from the three color channels is encoded and integrated, improving retrieval accuracy while preserving computational efficiency and response time. As images propagate through the hierarchical layers of the CNNs, increasingly abstract feature maps are produced, forming distinctive feature signatures that represent the visual content. These signatures are then reorganized into a newly constructed feature matrix designed to encode spatial relationships, chromatic properties, and latent structural patterns within the image. This enriched representation provides a more holistic description of image content, making it suitable for content-based image retrieval (CBIR) and visual similarity analysis. To validate the effectiveness and generalization capability of the proposed method, experiments were conducted on four widely recognized benchmark datasets: Corel-1K, CIFAR-10, 17-Flowers, and ZuBuD . The evaluation considered both retrieval precision and computational performance across datasets with varying resolutions, object categories, and scene complexities. Experimental results indicate that all three CNN architectures produce discriminative descriptors; however, DenseNet-201 consistently delivered superior performance on the CIFAR-10 dataset, which includes diverse object classes and varying image scales. Its densely connected architecture promotes improved feature reuse and gradient flow, leading to higher classification accuracy and more robust retrieval outcomes compared to GoogleNet and Inception V3. Overall, the proposed CNN-based descriptor generation framework demonstrates strong potential for scalable and accurate image retrieval applications in multimedia databases and intelligent visual search systems.
Copyright (c) 2026 Salahuddin, Muhammad Faisal Sohail, Hassan Raza Khan, Muhammad Tanveer Meeran (Author) https://creativecommons.org/licenses/by/4.0
THE INTERPLAY OF METACOGNITIVE BELIEFS AND THREAT MONITORING BIAS IN GENERALIZED ANXIETY DISORDER: A COGNITIVE–ATTENTIONAL PERSPECTIVE
Generalized Anxiety Disorder (GAD) is characterized by excessive and persistent worry accompanied by heightened sensitivity to perceived threats. Contemporary cognitive models suggest that anxiety is not only maintained by negative thought content but also by dysfunctional beliefs about thinking itself, known as metacognitive beliefs. From a cognitive–attentional perspective, these metacognitive beliefs interact closely with attentional processes, particularly threat monitoring bias, to sustain anxiety symptoms. Individuals with GAD often hold negative metacognitive beliefs regarding the uncontrollability and danger of worry, as well as positive beliefs that worry is useful for preventing harm. These beliefs encourage continuous monitoring of internal thoughts and external environments for potential threats. Threat monitoring bias refers to the tendency to preferentially attend to threat-related stimuli, such as negative words or fearful faces. Empirical research using experimental paradigms, including dot-probe and eye-tracking tasks, demonstrates that individuals with GAD show enhanced vigilance toward threatening information compared to non-anxious individuals. The interaction between metacognitive beliefs and threat monitoring bias forms a self-perpetuating cycle, commonly described within the Cognitive–Attentional Syndrome (CAS). It reviews empirical evidence supporting this interaction and discusses its implications for understanding anxiety maintenance. The paper further highlights the relevance of metacognitive therapy as an intervention that targets dysfunctional metacognitive beliefs and attentional control processes, offering a promising approach for reducing chronic worry and anxiety in GAD.
Copyright (c) 2026 Sana Fiaz, Ali Sher Abid Kayani, Aqsa Sahar, Mahnoor Haider Ramay, Iqra Bibi (Author) https://creativecommons.org/licenses/by/4.0
FACTORS CONTRIBUTING TO FEMALE MARGINALIZATION IN PAKISTANI HOUSEHOLDS: GENDERED PROBLEMS ENCOUNTERED BY WOMEN FROM PAKISTANI DIASPORA IN CANADA
The present study aimed to explore the factors contributing to female marginalization in Pakistani households. Opinions and views of lived experiences of women from the Pakistani diaspora in Canada were gathered to analyze if their situation is any different from that of women residing in Pakistan and from the international migration theory suggested by Boyd and Grieco in 2003. Qualitative research using the phenomenological approach was used to investigate the lived experiences of eight Pakistani women from the most densely populated Pakistani neighborhoods across Canada. The study focused on elite urban women who were university educated or were enrolled in a university degree program. Thematic analysis has been used to analyze the data. Through data analysis on problems encountered, eight associated categories emerged: threatening jokes, demeaning remarks, negative comparisons, expressions of opinion, marital expectations, struggle for education, female child trauma and matriarchy. The study conducted is at the intersection of gender, migration, and racialization. Women from the Pakistani diaspora in Canada experience structural barriers that limit social participation, economic mobility, and mental well-being. Addressing these inequities requires policy frameworks that integrate mental health and social development supports, flexible labor market access, equitable representation, and empowerment-oriented education focused on rights and opportunities in Canada.
Copyright (c) 2026 Taalia Khan (Author) https://creativecommons.org/licenses/by/4.0
Influence of Iqbal’s Educational Philosophy on Holistic Development: The Mediating Role of Transformative Self-Development and Self-Actualization
Dr. Allama Muhammad Iqbal, one of the most prominent Muslim philosophers, scholars, and reformers, presented a revolutionary Islamic educational philosophy whose principles continue to be researched and applied within contemporary educational systems worldwide. Pakistan’s existing education system, in particular, can benefit from embracing Iqbal’s transformative educational vision. The significance of this study lies in its emphasis on the purpose of youth existence, enabling them to reclaim their lost dignity and assume global leadership, as envisioned in the concept of the “Perfect Man.” The primary objective of this study is to examine the influence of Iqbal’s educational philosophy on holistic human development, with a specific focus on the mediating roles of transformative self-development and self-actualization. By highlighting these mediating constructs, the study underscores their critical role in fostering holistic development that balances intellectual, spiritual, emotional, and social dimensions of education. Such an approach enables the younger generation to cultivate spiritual awareness, intellectual growth, and moral consciousness. A quantitative research approach was employed to examine the impact of Iqbal’s Educational Philosophy (IEP) on Holistic Development (HD), mediated by Self-Actualization (SA) and Transformative Self-Development (TSD). Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze direct and indirect relationships among the constructs using Smart PLS (version 24). The study population comprised undergraduate students from two public universities in Karachi. The randomly selected sample included 120 undergraduates (60 males and 60 females).The findings reveal a growing interest among students in Iqbal’s concepts of self-actualization and transformative self-development, which help them recognize their inner potential and accept responsibility for personal and societal transformation. The study recommends incorporating Iqbal’s educational philosophy into curricula to cultivate adaptable, socially responsible, and future-ready generations.
Copyright (c) 2026 Nosheen Rehman, Dr. Fayyaz Ahmad Shaheen, Farooq Ahmed (Author) https://creativecommons.org/licenses/by/4.0
PHOSPHORUS FRACTION DYNAMICS IN SOIL AS AFFECTED BY PHOSPHORUS-SOLUBILIZING BACTERIA AND INTEGRATED PHOSPHORUS FERTILIZATION
Phosphorus (P) availability in agricultural soils is limited by fixation into mineral and organic pools, reducing fertilizer use efficiency. This study evaluated the capacity of a phosphorus-solubilizing bacterial inoculant (Pseudomonas striata) combined with integrated organic–inorganic P sources to modify soil P fraction dynamics under controlled incubation. Soil (0–20 cm) was incubated in 2-kg units amended with seven treatments (T1 = control; T2 = rock phosphate; T3 = SSP; T4 = compost + RP; T5 = compost + SSP; T6 = bagasse ash + RP; T7 = bagasse ash + SSP) with and without PSB. PSB inoculation and treatment composition significantly affected all measured P fractions and their temporal trends. At early sampling (10 d), integrated SSP + bagasse ash (T7) and SSP + compost (T5) produced the largest increases in labile and moderately labile pools: e.g., ABDTPA-P (T7 = 5.70 vs control 2.74 mg g⁻¹) and Olsen-P (T5 = 8.04 vs control 4.28 mg g⁻¹). Resin-P and ethanol-extractable P followed similar patterns (resin-P T7 = 7.00 vs control 2.49 mg g⁻¹; ethanol extractable P T7 = 0.836 vs control 0.174 mg L⁻¹). Although all P pools declined with incubation time due to sorption and biological uptake, treatments T5, T7 and (in later intervals) T6 retained relatively higher P concentrations at 40–60 d. Across intervals, PSB-inoculated soils consistently showed greater P availability than non-inoculated controls (e.g., mean ABDTPA-P increase ≈ 6–8%), indicating persistent microbial enhancement of P solubilization and labile pool maintenance. The results demonstrate that PSB coupled with SSP blended with compost or bagasse ash significantly increases short-term P availability and prolongs residence of labile P in alkaline soils. Our findings support integrated biological–organic strategies to improve phosphorus use efficiency and provide a mechanistic basis for field evaluation and optimization of PSB-based P management in cropping systems.
Copyright (c) 2026 Salman Rahim, Maria Mussarat, Dost Muhammad (Author) https://creativecommons.org/licenses/by/4.0
EXPLORING PARENTS’ EXPERIENCES AND CHALLENGES IN MANAGING AUTISM SPECTRUM DISORDER (ASD) IN CHILDREN: A QUALITATIVE STUDY
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by a multifaceted interplay of genetic, biological, and environmental factors. Despite ongoing research, the etiology of ASD remains not fully understood. A review of existing literature indicates that environmental interactions, including exposure to toxins and air pollutants, may play a significant role in the onset of autism spectrum disorder (ASD). The frequency of ASD has been increasing, globally one in 127 people have autism in 2021, and now It is estimated that worldwide about 1 in 100 children have autism. For Pakistan, research on autism is on early developmental stage and so far, no reliable statistics available on the prevalence of ASD in Pakistan. The current study adopted qualitative approach. To conduct the interview, total of 14 participants (7 parents) was carefully chosen based on predetermined criteria. The age of parents ranges from 32-47 years. All respondents belong to different localities, four parents belong to Rawalpindi and Islamabad, two belong to AJK and one belongs to Gujrat, Pakistan. According to research, difficulty in social interaction, attention challenges, and aggression are the most common problems that children with autism. The state and health departments should create community-based awareness programs that are exclusively focused on genetic health and ensure sensitive genetic counselling for society and involve community influencers, educators, and religious leaders in communication strategies. Sustainable ASD policy requires a whole-family approach that integrates health, social protection, and inclusion strategies.
Copyright (c) 2026 Usman Ahmad (Author) https://creativecommons.org/licenses/by/4.0
EXPLORING THE THERAPEUTIC EFFICACY OF CUMIN EXTRACT AGAINST UTI-CAUSING PATHOGENS
In recent years, treatment of urinary tract infection has become increasingly challenging due to the prevalence of antimicrobial resistance patterns in the UTI causing pathogens. Plant extracts could be used to increase the efficiency of the conventional antibiotics without increasing antibiotics doses due to the presence of bioactive compounds. The current study has explored the potential of cumin seed extract to inhibit the growth of UTI pathogens. The potential was assessed using solvent including methanol, ethanol, acetone and water at different duration including 24, 48 and 72 hours for each extract. The pathogens included Escherichia coli, Proteus vulgaris, Klebsiella pneumoniae, and Staphylococcus aureus. It was observed that the cumin seed extract exhibited antimicrobial activity against all tested pathogens. Results demonstrates that methanol extracts exhibited stronger antimicrobial potential with the highest zones of inhibition observed for P. vulgaris and E. coli (19mm), K. pneumoniae (18mm) and S. aureus (15mm). Ethanol extracts also exhibited significant antimicrobial potential particularly against K. pneumoniae (18mm) and E. coli (17mm). Acetone and distilled water showed comparatively lower activity. The results proved that the cumin seed extract could be used as a potential source of antimicrobial agent, suggesting a promising compound for healthcare applications, especially antimicrobial resistance.
Copyright (c) 2026 Areeja Ansari, Syeda Bushra Zafar, Samina Iqbal, Syeda Hafiza Afsheen Zafar, Mehwish Faiz, Dr Muhammad Ahsan Baig, Dr Asadullah Khokar (Author) https://creativecommons.org/licenses/by/4.0
EXPERIMENTAL INVESTIGATION OF TITANIUM COATING FOR CORROSION RESISTANCE ON STAINLESS STEEL GRADE ASTM 304L AND 316L FOR MEDICAL APPLICATION
This research study investigates the surface enhancement of austenitic stainless steels ASTM 304L and 316L for biomedical implant applications through a titanium dioxide (TiO₂) thin film deposited by physical vapor deposition (PVD). This study aims to determine whether a TiO₂ coating can improve corrosion resistance in physiologically relevant environments while maintaining or enhancing key mechanical characteristics required for load-bearing medical components. Uncoated and coated specimens of both grades were evaluated using electrochemical corrosion testing, mechanical testing (hardness and compressive strength), and microstructural/chemical characterization by scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS). The TiO₂-coated samples exhibited a clear improvement in corrosion behavior compared with the uncoated substrates, indicating a more protective surface and reduced susceptibility to degradation. Mechanical results showed a modest increase in surface hardness and a slight improvement in compressive performance after coating, suggesting that the deposited layer contributed to better surface integrity without compromising bulk response. Among the investigated materials, TiO₂-coated 316L demonstrated superior overall performance relative to TiO₂-coated 304L, consistent with the higher intrinsic corrosion resistance of 316L and the effectiveness of the coating–substrate combination. SEM examination revealed a uniform, dense coating morphology with no visible cracking or delamination, while EDS confirmed the presence and continuity of the TiO₂ layer. These findings support PVD-deposited TiO₂ as a practical route to improve the corrosion resistance and surface performance of stainless steel alloys intended for biomedical implant service.
Copyright (c) 2026 Syed Nasir Mehdi Gardezi, Asad Raza Gardezi, Dr. Ali Nawaz Sanjrani, Muhammad Punhal Sahto, Muhammad Hassan, Arfa, Ali Raza (Author) https://creativecommons.org/licenses/by/4.0
TWO-PHASE NETWORK TRAFFIC INTRUSION DETECTION SYSTEM BASED ON THE RANDOM FOREST AND XGBOOST.
The article reports a dual-stage network intrusion detection system (IDS) that implements the use of Random Forest and XGBoost in order to enhance the identification of malicious traffic in modern networks. This system then conducts binary classification in order to differentiate between normal and anomaly traffic and subsequently usage of multi-class classification is done to distinguish between particular types of attacks. Both models are evaluated comprehensively using the UNSW-NB15 dataset that has forty-five engineered features and ten traffic classes. The process of data preprocessing involves missing value imputation, feature scaling, and one-hot encoding to make sure that the input is not compromised. When using experiments, it is observed that XGBoost slightly outperforms Random Forest on the multi-class task, with an accuracy of 80.74 and both models have an accuracy of more than 93 on binary detection. The two-step process reduces the impact of the imbalance of classes and gives interpretable results through the visualization of the confusion matrix. The work provides a machine-learned pipeline that can be deployed in the real world to monitor network traffic by making use of this work to reproduce the pipeline
Copyright (c) 2026 Shehla Shah, Muhammad Adil, Tauseef Noor, Waqar Nawaz, Sohail Farooq, Muhammad Arif Afridi, Yasir Adnan (Author) https://creativecommons.org/licenses/by/4.0
ESTIMATING AND PROJECTING PAKISTAN’S POPULATION GROWTH: A LOG - LINEAR REGRESSION MODEL APPROACH WITH CONFIDENCE INTERVALS.
This study analyzes the long-term population growth of Pakistan using a log-linear regression model estimated through Ordinary Least Squares (OLS). Annual population data are employed to capture the exponential growth pattern by modeling the natural logarithm of population as a function of time. The estimated time coefficient is positive and highly significant, confirming a persistent growth trend in Pakistan’s population. The model demonstrates an excellent fit, as reflected by a high coefficient of determination () and strong correlation between time and population. Diagnostic statistics, including the F-statistic and t-tests, indicate that the regression model and its parameters are statistically significant at the 1% level. Residual analysis further supports the adequacy of the model assumptions. Based on the estimated growth rate, population forecasts are generated for the period . The projections indicate continued population expansion, posing challenges related to resource allocation and urbanization. The findings highlight the effectiveness of log-linear regression for demographic forecasting. This study provides valuable evidence to support population planning and sustainable development policies in Pakistan.
Copyright (c) 2026 Zohaib Ali, Syeda Hira Fatima Naqvi, Muzaffar Hussain Laghari, Abdul Rafiu Alias Furkan (Author) https://creativecommons.org/licenses/by/4.0
ASSESSMENT OF ANTIOXIDANT AND ANTI-MALARIAL COMPONENTS IN FRUIT SEEDS USING UV-VISIBLE SPECTROSCOPY
This study evaluates the anti-oxidant as well as anti-malarial effects of bioactive constituents determined in various fruit seeds, particularly caper berry, garlic, almond, walnut, melon and watermelon collected from Taluka Faiz Ganj, District Khairpur Mir’s, Sindh, Pakistan. Fruit seeds, which contain useful phytochemicals that could be used in the coordination of the circular economy, are typically discarded as agricultural waste. UV-visible spectroscopy was used to measure four main parameters: rutin, gallic acid, artemether, and quercetin. The maximum concentrations of all the chemicals under study were observed in walnut seeds including Rutin (20.20 mg/g), Gallic acid (2.06 mg/g), Artemether (3.09 mg/g), and Quercetin (20.61 mg/g). Melon as well as watermelon seeds also declared to be useful contributors of these bioactive components, with almonds possessing the minimum amounts. These results provide scientific support to the conventional therapeutic practice of these seeds and specify hopeful opportunities for changing agricultural byproducts into efficient food constituents, nutraceuticals, and medicines to address chronic illnesses and oxidative stress.
Copyright (c) 2026 Aijaz Ali Shar, Dr. Abdul Raheem Shar, Prof. Dr. Ghulam Qadir Shar, Seema Sarwar Ghumro, Ammat-ur-Rehman Soomro, Rabia Parveen Memon, Hajra Kharal, Sahib Ghanghro, Farzana Mangrio (Author) https://creativecommons.org/licenses/by/4.0
DEVELOPMENT OF SUSTAINABLE BRICKS UTILIZING INDUSTRIAL WASTE
The viability of clay bricks for construction applications is significantly influenced by their compressive strength and water absorption characteristics. This study evaluated the impact of various additives—including corncob ash (CCA), wheat husk ash (WHA), ceramic powder (CP), bagasse ash (BA), and rice husk ash (RHA)—on the mechanical and durability properties of clay bricks. The results indicated that although all bio-based ashes (CCA, WHA, BA, and RHA) contain silica, their effects on compressive strength vary due to differences in particle size, chemical composition, and interactions with the clay matrix. CCA exhibited a decreasing strength trend with increasing content, dropping from 14.27 MPa at 1.5% to 9.91 MPa at 12%, mainly due to increased porosity. WHA showed an initial increase in strength but declined at higher percentages, following a similar trend. In contrast, CP enhanced compressive strength at all levels, peaking at 15.27 MPa at 12%, attributed to its finer texture and ability to densify the brick matrix. BA and RHA followed a trend similar to that of CCA and WHA, in which strength increased slightly at lower percentages but decreased above 6%, primarily due to increased void formation. Water absorption analysis revealed that bio-based ashes increased porosity and water uptake, with CCA exhibiting the highest water absorption of 18.20% at 12%. WHA and BA exhibited similar patterns, resulting in increased permeability. Conversely, CP significantly reduced water absorption across all percentages, reaching a minimum of 8.13% at 1.5%, due to its ability to enhance particle packing and reduce voids. Overall, CP proved to be the most effective additive for improving strength and water resistance, while excessive amounts of bio-based ashes compromised the structural integrity of bricks. These findings provide insights into optimizing additive proportions for sustainable clay brick production while balancing mechanical performance and durability.
Copyright (c) 2026 Faraz Ahwaz, Khwaja Adeel Tariq, Muhammad Shoaib Ashraf, Syed Kamran Hussain Shah, Ahmad Raza (Author) https://creativecommons.org/licenses/by/4.0
INTEGRATED APPLICATION OF BIO-STIMULANTS AND PHOSPHORUS ENHANCES PHENOLOGY, GROWTH, AND YIELD OF MAIZE
Maize productivity is often constrained by suboptimal nutrient availability and poor early crop establishment, which can limit growth, reproductive development, and yield. This study investigated the effects of varying levels of bio-stimulants (0, 8, 10, and 12 L ha⁻¹) and phosphorus (0, 60, 90, and 120 kg ha⁻¹) on maize phenology, growth, and yield attributes at Charbagh Nursery Farm, Swabi, during Kharif 2024 using a randomized complete block (RCB) design with four replications. The results showed that both factors significantly influenced maize development, with notable interactions for several traits. Bio-stimulant application accelerated seed emergence, increased single leaf area, and enhanced plant height, while higher phosphorus levels improved leaf area and growth vigor. Tasseling and silking were delayed by bio-stimulants, allowing prolonged vegetative growth, with the combined application of 12 L ha⁻¹ bio-stimulant and 120 kg ha⁻¹ phosphorus producing the greatest delay. Grain yield components, including grains per ear and thousand-grain weight, as well as biological and grain yields, were significantly enhanced under higher levels of both factors, with the highest values recorded under combined application (408 grains ear⁻¹, 252.7 g thousand-grain weight, 12,958 kg ha⁻¹ biological yield, and 4,326 kg ha⁻¹ grain yield). Harvest index was also improved, reaching 41.6% under 10 L ha⁻¹ bio-stimulant × 90 kg ha⁻¹ phosphorus. In conclusion, integrating bio-stimulants with phosphorus at optimal levels enhances early crop establishment, promotes reproductive development, and maximizes maize yield, offering a practical strategy for sustainable intensification of maize production.
Copyright (c) 2026 Saad Saud Jan, Prof. Dr. Abdul Basir, Dr. Muhammad Ibrahim, Majid Ali Jan, Rashid Ali Jan, Salman Khan, Muhammad Basit (Author) https://creativecommons.org/licenses/by/4.0
BACTERIAL THERAPY FOR CANCER: A REVIEW OF MECHANISMS, APPLICATIONS, AND FUTURE PROSPECTS
Cancer remains one of the leading causes of morbidity and mortality worldwide, despite major advances in surgery, chemotherapy, radiotherapy, and immunotherapy. However, many conventional treatments are associated with serious side effects, drug resistance, and limited specificity for tumor cells. In recent years, bacterial therapy has re-emerged as a promising alternative or complementary strategy for cancer treatment. Certain bacteria have the natural ability to selectively target tumors, stimulate anti-tumor immune responses, and deliver therapeutic molecules directly to cancer cells. This review discusses the historical background, mechanisms of action, types of bacteria used in cancer therapy, genetic engineering approaches, clinical applications, challenges, and future directions of bacterial-based cancer therapy.
Copyright (c) 2026 Marwana Magaji, Ashiru Magaji (Author) https://creativecommons.org/licenses/by/4.0
A HYBRID SCENARIO-BASED PROBABILISTIC FRAMEWORK FOR LONG-TERM ELECTRICITY DEMAND FORECASTING IN PAKISTAN (2025-2060)
Pakistan’s electricity sector faces chronic challenges, including a persistent supply demand gap, circular debt exceeding PKR 2.5 trillion, and the imperative to integrate variable renewable energy sources. Accurate long-term demand forecasting is critical for infrastructure planning; however, existing deterministic approaches consistently overestimate demand and do not account for structural changes, such as behind-the-meter (BTM) solar adoption and efficiency improvement. This study presents a hybrid scenario-based probabilistic forecasting framework that combines ensemble machine learning with Monte Carlo simulations to generate sector-wise electricity demand projections for Pakistan from 2025 to 2060. The framework decomposes demand into five sectors (residential, commercial, industrial, agricultural, and other) and evaluates six policy-relevant scenarios. Uncertainty was quantified using 2,000 Monte Carlo draws propagating parameter uncertainty from GDP growth, population dynamics, climate sensitivity, and model residuals. Under the Business-as-Usual (BAU) scenario, the P50 demand reaches 185 TWh by 2030 (P10-P90:165-210 TWh), 295 TWh by 2040, 420 TWh by 2050, and 580 TWh by 2060. The High Solar/BTM scenario reduces grid demand by 15-22% relative to BAU by 2050, whereas the High Heat scenario increases peak demand by 12-18%. These probabilistic projections provide actionable inputs for Pakistan’s Indicative Generation Capacity Expansion Plan (IGCEP), NEPRA regulatory planning, and Nationally Determined Contribution (NDC) commitments under the Paris Agreement.
Copyright (c) 2026 Sher Muhammad Ghoto, Nadeem Ahmed Tunio, Saad Raza Malik, Huzaifa Aslam, Sajjad Bhangwar (Author) https://creativecommons.org/licenses/by/4.0
COMPUTATIONAL OPTIMIZATION METHODS FOR RENEWABLE AND SUSTAINABLE ENERGY SYSTEMS: A COMPREHENSIVE REVIEW
Social and economic development depends on sustainable energy as one of the important inputs. The demand of energy has therefore risen drastically due to the generalization of agricultural, industrial, and domestic activities, particularly in the emergent nations. This has translated to fast acceleration in the amount of greenhouse gas emissions and the rise in fuel prices that are the primary driving factor behind the push to harness renewable energy sources more optimally, i.e., the energy that is produced by natural resource and also naturally replenished. Although the benefits of renewable energy are apparent, it also has significant demerits, including the lack of consistency in production, since a majority of the renewable energy sources are climate-dependent, and that is why their utilization needs sophisticated design, planning, and optimization of control strategies. Fortunately, with the ever growing developments in computer hardware and software’s, researchers are getting access to these optimization problems through the use of computational resources, which is illustrated by the fact that there are many optimization methods that have been implemented in the renewable and sustainable energy sector. The paper provides an analysis of the latest state of the art in numerical optimization procedures used in renewable and sustainable energy, which can help to see the recent research developments in this area.
Copyright (c) 2026 Zahid Zahid, Ali Nawaz Sanjrani, Sadiq Ali Shah, Attaullah Narejo, Shah Dad Hasil (Author) https://creativecommons.org/licenses/by/4.0
HYBRID IDENTITIES AS POLITICAL RESISTANCE: DECONSTRUCTING EAST–WEST DICHOTOMIES
Hybrid identities have emerged as a powerful form of political resistance in postcolonial societies, challenging rigid East–West binaries that have historically structured global power relations. Drawing on postcolonial theory, cultural studies, and South Asian intellectual traditions, this article examines hybridity as a dynamic process through which marginalized subjects negotiate, reinterpret, and subvert dominant cultural narratives. Focusing on the Pakistani context, the study argues that hybrid identities do not signify cultural loss or confusion but instead represent strategic forms of agency that contest colonial epistemologies and nationalist essentialism. By deconstructing East–West dichotomies, hybrid subjectivities create alternative spaces of belonging that disrupt hegemonic representations of culture, modernity, and tradition. The article demonstrates how hybridity operates across literature, language, politics, and everyday practices, functioning as both a site of tension and a resource for resistance. Ultimately, the study reframes hybridity as a critical tool for understanding contemporary political struggles in an increasingly interconnected yet unequal world.
Copyright (c) 2026 Gulraiz Falak, Muhammad Rizwan (Author) https://creativecommons.org/licenses/by/4.0